From 09c0e2fb720b734524ae62d3be6717b35442f402 Mon Sep 17 00:00:00 2001
From: "Kamal Bentahar (ab3735)" <ab3735@coventry.ac.uk>
Date: Thu, 11 Feb 2021 13:58:22 +0000
Subject: [PATCH] Update README.md

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-# TSP-Guidance
-Guide for the 380CT Assignment on TSP
+# Guide for the 380CT Assignment on TSP
+
+The actual part you need to submit is the **Metaheuristics section**.
+The rest is meant to introduce you to the basics.
+
+## Lab 5
+
+- Ensure you have **Jupyter**.
+	- Either install [Jupyter](https://jupyter.org/install) alone or [Anaconda](https://www.anaconda.com/distribution).
+- Familiarise yourself with Jupyter functionaility. Consider taking **LinkedIn Learning courses** (free through the university) or any suitable alternatives. Here is a recommended set (e.g. each member of the group takes one):
+
+	- [Introducing Jupyter](https://www.linkedin.com/learning/introducing-jupyter/present-data-like-a-pro-with-jupyter)
+	- [Get Ready for Your Coding Interview](https://www.linkedin.com/learning/get-ready-for-your-coding-interview/welcome)
+	- [Python for Data Visualization](https://www.linkedin.com/learning/python-for-data-visualization/setting-marker-type-and-colors)
+	- [Python: Programming Efficiently](https://www.linkedin.com/learning/python-programming-efficiently/time-profiling)
+	- [Python Statistics Essential Training](https://www.linkedin.com/learning/python-statistics-essential-training/the-power-of-visualization)
+
+- Load and study `Investigating TSP.ipynb`.
+	- Can you improve any of the functions to make them more efficient?
+	- See how large you can make _n_ while testing `exhaustive_search()`.
+	- Check that `greedy_nearest_neighbours()` is correct. If not then fix it!
+
+- Read the [Wikipedia article on TSP](https://en.wikipedia.org/wiki/Travelling_salesman_problem). Pay attention to th **Computing a solution** section, and especially to the `2-opt` and `3-opt` techniques for defining neighbourhoods.
+
+- Experiment with generating your own graph families. For example:
+	- **Euclidean graphs**: generate points using _(x,y)_ coordinates, then generate the adjacency matrix by calculating all the required distances. Recall that the distance between two points _(x<sub>1</sub>,y<sub>1</sub>)_ and _(x<sub>2</sub>,y<sub>2</sub>)_ is _sqrt[(x<sub>1</sub>-x<sub>2</sub>)<sup>2</sup>+(y<sub>1</sub>-y<sub>2</sub>)<sup>2</sup>]_.
+	- **Graphs with obvious shortest cycle**: think of a graph where all the distances are 2 except for the edges on a predefined cycle, where the distance is 1. Such a graph would be useful for testing/debugging the *nearest neighbours greedy search*.
+
+
+
+## Bibliography
+
+- Applegate, DL, Bixby, RE, Chvátal, V, Cook, WJ, 2007, [The Traveling Salesman Problem: A Computational Study](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5199622620002011), Princeton University Press, Princeton.
+- Cook, WJ 2012, [In Pursuit of the Traveling Salesman: Mathematics at the Limit of Computation](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5199665280002011), Princeton University Press, Princeton.
+- Glover, F, & Kochenberger, GA (eds) 2002, [Handbook of Metaheuristics](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51109755880002011), Kluwer Academic Publishers, Secaucus.
+- Gutin, G, & Punnen, AP (eds) 2002, [The Traveling Salesman Problem and Its Variations](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51125059450002011), Springer, New York, NY.
+- Pintea, C.-M., 2014. [Advances in Bio-inspired Computing for Combinatorial Optimization Problems](https://locate.coventry.ac.uk/permalink/f/1r06c36/COV_ALMA5155140430002011). 1st ed. 2014.
+- Steven, SS 2008, [The Algorithm Design Manual](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA5190160580002011), Springer, England.
+	- You may also find its [companion website](http://algorist.com/problems/Traveling_Salesman_Problem.html) useful.
+- Talbi, E.-G., 2009. [Metaheuristics from design to implementation](https://locate.coventry.ac.uk/permalink/f/gr8698/COV_ALMA51117060170002011), Hoboken, NJ: John Wiley & Sons.